to use AUC-ROC: comparing classifiers across all thresholds

to use random forests: when you want a strong baseline with minimal hyperparameter tuning

Random forests are ideal for robust baseline models with minimal hyperparameter tuning

to use F1 score: when classes are imbalanced and both FP and FN matter

Use F1 score when classes are imbalanced and both FP and FN matter

score matching does: learns the gradient of the log-density without normalizing

Matching score learns gradient of log-density without normalizing

to standardize: when you need zero mean and unit variance for gradient-based optimization

Standardize when zero mean and unit variance are required for gradient-based optimization

to normalize features: when features have different scales and you use distance-based methods

Normalize features when they have different scales for distance-based methods

mixed precision training does: forward in FP16, accumulate gradients in FP32

Mixed precision training: forward in FP16, accumulate gradients in FP32